I am new to LSTM and deep learning. I have
3000 reviews which I am trying to train on gensim pretrained model via word embedding. I have the following model where max_sequence_size=564.
max_sequence_size = max_features classes_num = 1 my_model=Sequential() my_model.add(Embedding(max_sequence_size,300,input_length=trainDataVecs.shape,weights=[embedding_matrix])) my_model.add(Bidirectional(LSTM(100,activation='tanh',recurrent_dropout = 0.2, dropout = 0.2))) my_model.add(Dense(50,activation='sigmoid')) my_model.add(Dropout(0.2)) my_model.add(Dense(classes_num, activation='softmax')) print "Compiling..." my_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) print my_model.summary()
I saw the link in which he is saying to change the optimizer which doesn't help.
Should I add more layer since this might be shallow? Is there any way in which I can print the gradients?